docs: def calc_bias_correction_bca(bootstrap_replicates, mle_estimate):
      """
      Calculate the bias correction for the Bias Corrected and Accelerated (BCa)
      bootstrap confidence intervals.
  
      Parameters
      ----------
      bootstrap_replicates : 2D ndarray.
          Each row should correspond to a different bootstrap parameter sample.
          Each column should correspond to an element of the parameter vector
          being estimated.
      mle_estimate : 1D ndarray.
          The original dataset's maximum likelihood point estimate. Should have
          one elements for each component of the estimated parameter vector.
  
      Returns
      -------
      bias_correction : 1D ndarray.
          There will be one element for each element in `mle_estimate`. Elements
          denote the bias correction factors for each component of the parameter
          vector.
  
      References
      ----------
      Efron, Bradley, and Robert J. Tibshirani. An Introduction to the Bootstrap.
          CRC press, 1994. Section 14.3, Equation 14.14.
      """
      numerator = (bootstrap_replicates < mle_estimate[None, :]).sum(axis=0)
      denominator = float(bootstrap_replicates.shape[0])
      bias_correction = norm.ppf(numerator / denominator)
      return bias_correction